tea_tasting.config
#
Global configuration.
get_config(option=None)
#
Retrieve the current settings of the global configuration.
Parameters:
Name  Type  Description  Default 

option 
str  None

The option name. 
None

Returns:
Type  Description 

Any

The specified option value if its name is provided, or a dictionary containing all options otherwise. 
Examples:
Source code in src/tea_tasting/config.py
set_config(*, alpha=None, alternative=None, confidence_level=None, equal_var=None, n_obs=None, n_resamples=None, power=None, ratio=None, use_t=None, **kwargs)
#
Update the global configuration with specified settings.
Parameters:
Name  Type  Description  Default 

alpha 
float  None

Significance level. Default is 0.05. 
None

alternative 
Literal['twosided', 'greater', 'less']  None

Alternative hypothesis. Default is 
None

confidence_level 
float  None

Confidence level for the confidence interval.
Default is 
None

equal_var 
bool  None

Defines whether equal variance is assumed. If 
None

n_obs 
int  Sequence[int]  None

Number of observations in the control and in the treatment together.
Default is 
None

n_resamples 
int  None

The number of resamples performed to form the bootstrap
distribution of a statistic. Default is 
None

power 
float  None

Statistical power. Default is 0.8. 
None

ratio 
float  int  None

Ratio of the number of observations in the treatment relative to the control. Default is 1. 
None

use_t 
bool  None

Defines whether to use the Student's tdistribution ( 
None

kwargs 
Any

Userdefined global parameters. 
{}

Examples:
import tea_tasting as tt
tt.set_config(equal_var=True, use_t=False)
experiment = tt.Experiment(
sessions_per_user=tt.Mean("sessions"),
orders_per_session=tt.RatioOfMeans("orders", "sessions"),
orders_per_user=tt.Mean("orders"),
revenue_per_user=tt.Mean("revenue"),
)
experiment.metrics["orders_per_user"]
#> Mean(value='orders', covariate=None, alternative='twosided',
#> confidence_level=0.95, equal_var=True, use_t=False)
Source code in src/tea_tasting/config.py
config_context(*, alpha=None, alternative=None, confidence_level=None, equal_var=None, n_obs=None, n_resamples=None, power=None, ratio=None, use_t=None, **kwargs)
#
A context manager that temporarily modifies the global configuration.
Parameters:
Name  Type  Description  Default 

alpha 
float  None

Significance level. Default is 0.05. 
None

alternative 
Literal['twosided', 'greater', 'less']  None

Alternative hypothesis. Default is 
None

confidence_level 
float  None

Confidence level for the confidence interval.
Default is 
None

equal_var 
bool  None

Defines whether equal variance is assumed. If 
None

n_obs 
int  Sequence[int]  None

Number of observations in the control and in the treatment together.
Default is 
None

n_resamples 
int  None

The number of resamples performed to form the bootstrap
distribution of a statistic. Default is 
None

power 
float  None

Statistical power. Default is 0.8. 
None

ratio 
float  int  None

Ratio of the number of observations in the treatment relative to the control. Default is 1. 
None

use_t 
bool  None

Defines whether to use the Student's tdistribution ( 
None

kwargs 
Any

Userdefined global parameters. 
{}

Examples:
import tea_tasting as tt
with tt.config_context(equal_var=True, use_t=False):
experiment = tt.Experiment(
sessions_per_user=tt.Mean("sessions"),
orders_per_session=tt.RatioOfMeans("orders", "sessions"),
orders_per_user=tt.Mean("orders"),
revenue_per_user=tt.Mean("revenue"),
)
experiment.metrics["orders_per_user"]
#> Mean(value='orders', covariate=None, alternative='twosided',
#> confidence_level=0.95, equal_var=True, use_t=False)